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Apple Intelligence Foundation Language Models: Tech Report 2025
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Apple Intelligence Foundation Language Models: Tech Report 2025
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We introduce two multilingual, multimodal foundation language models that power Apple Intelligence features across Apple devices and services: i a 3B-parameter on-device model optimized for Apple silicon through architectural innovations such as KV-cache sharing and 2-bit quantization-aware training; and ii a scalable server model built on a novel Parallel-Track Mixture-of-Experts PT-MoE transformer that combines track parallelism, mixture-of-experts sparse computation, and interleaved global-local attention to deliver high quality with competitive cost on Apple's Private Cloud Compute platform. Both models are trained on large-scale multilingual and multimodal datasets sourced via responsible web crawling, licensed corpora, and high-quality synthetic data, then further refined with supervised fine-tuning and reinforcement learning on a new asynchronous platform. The resulting models support several additional languages while understanding images and executing tool calls. In public benchmarks and human evaluations, both the server model and the on-device model match or surpass comparably sized open baselines. A new Swift-centric Foundation Models framework exposes guided generation, constrained tool calling, and LoRA adapter fine-tuning, allowing developers to integrate these capabilities with a few lines of code. The latest advancements in Apple Intelligence models are grounded in our Responsible AI approach with safeguards like content filtering and locale-specific evaluation, as well as our commitment to protecting our users' privacy with innovations like Private Cloud Compute.
Forward citations
Cited by 8 Pith papers
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
FlexServe introduces recallable secure memory and NPU to enable cooperative secure LLM inference on mobile devices, reporting 10.05X TTFT speedup over a basic TrustZone strawman.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
FlexServe decouples access and management of secure resources in TrustZone to enable efficient LLM inference on mobiles, reporting 10.05X TTFT speedup over basic strawman designs and 2.44X over optimized ones.
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Unlocking Apple's Private Cloud Compute: An Analysis of Privacy-Preserving Artificial Intelligence
Researchers reverse-engineer Apple's Private Cloud Compute to evaluate privacy guarantees, open custom query interfaces, benchmark the model, and release a public framework.
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Peak-Detector: Explainable Peak Detection via Instruction-Tuned Large Language Models in Physiological Sign
Peak-Detector uses instruction-tuned LLMs and a condensed peak-representation of time-series data to achieve robust cross-modal peak detection with self-generated explanations across ECG, PPG, BCG, and BSG signals.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
Page-granular Flex-Mem and switchable Flex-NPU cut TrustZone LLM TTFT by ~10× vs a CMA strawman and ~2.4× vs a pipelined secure-NPU strawman on RK3588.
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Punching Above Their Weight: Classification-Head Fine-Tuning of Tiny Language Models (TLMs) for Verifiable Multiple-Choice Tasks
Classification-head LoRA fine-tuning of sub-3B Qwen3 models outperforms label-generation SFT by 2–3% on HellaSwag, WinoGrande and PIQA and yields SOTA numbers competitive with GPT-3/PaLM/GPT-4.
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FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation
FlexServe achieves up to 10x faster time-to-first-token for secure LLM inference on mobile devices by using flexible resource isolation in TrustZone compared to standard approaches.
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Forget to Improve: On-Device LLM-Agent Continual Learning via Budget-Curated Memory
A net-value-per-byte curator governs memory lifecycle in on-device LLM agents, cutting memory 2.7x and uplink 2.4x while driving injection success to zero on task-drift benchmarks and Jetson hardware.
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